Robust Recommendation Algorithm Based on Kernel Principal Component Analysis and Fuzzy C-means Clustering

Yi, Huawei; Niu, Zaiseng; Zhang, Fuzhi; Li, Xiaohui; Wang, Yajun

doi:10.1007/s11859-018-1301-6

Cited by 16 publications

(4 citation statements)

References 15 publications

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“…(3) Literature [21] calculates the UICPR matrix by calculating the priority ratio of user item categories to reduce the dimension of data. Meanwhile, users are clustered, and the closest users are found so as to obtain the predicted rating and make recommendations (4) Literature [22] Literature [20] Literature [21] Literature [22] Proposed Mae value…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

The Application of Personalized Recommendation System in the Cross-Regional Promotion of Provincial Intangible Cultural Heritage

Wang

2022

Advances in Multimedia

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With the accelerated trend of globalization and modernization, many provincial intangible cultural heritages (PICH) are in danger of being lost. In the context of Internet technology, the use of digital multimedia for personalized recommendation is an effective way to promote the transmission of intangible cultural heritage. However, traditional recommendation systems tend to treat different members as homogeneous objects, ignoring the relationship between members’ professional backgrounds and the inherent properties of items, and cannot truly solve the problem of conflicting preferences in the integration process. In view of this, this paper proposes a group recommendation system based on nonnegative matrix decomposition. First, the group rating information is decomposed into a user matrix and item matrix by nonnegative matrix decomposition. Then, the item affiliation matrix and member expertise matrix are calculated by using the affiliation and expertise weights for the two matrices, respectively, and the contribution degree of each member to different item ratings is obtained from them. Finally, the group preference model is constructed by weighted fusion of members’ preferences based on their contribution degrees, and different recommendation lists are generated for different user preferences. The experimental results prove that this system has high recommendation accuracy in cross-regional promotion of PICH.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

The Application of Personalized Recommendation System in the Cross-Regional Promotion of Provincial Intangible Cultural Heritage

Wang

2022

Advances in Multimedia

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“…The experimental results show that this method has more advantages for the recall rate, accuracy rate and false positive rate among different attacks. Li et al proposed an algorithm that first uses kernel principal component analysis to reduce the dimension of user rating matrix, and then uses fuzzy c-mean clustering method to distinguish normal users from shilling attack users [14]. Yi et al proposed a new suspicious user measurement method, which uses the correlation vector machine classifier to identify and measure suspicious users, and uses the information of suspicious users to build a multi-dimensional trust model [13].…”

Section: Related Workmentioning

confidence: 99%

Robust Recommendation Algorithm Based on User Suspicious Probability and Item Weight

Gao¹,

Liu²,

Zheng³

2022

Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2

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With the extensive development of recommendation technology, the threat of shilling attacks faced by the existing collaborative recommendation algorithms is also increasing sharply. To face more and more complex shilling attacks, this paper constructs a robust recommendation algorithm that can resist shilling attacks from the perspective of recommendation algorithm. Existing robust recommendation algorithms usually improve robustness by sacrificing some recommendation accuracy and reduce the recommendation accuracy. To solve this problem, this paper proposes a robust recommendation algorithm based on user suspicious probability and item weight. Firstly, we establish the relevance vector machine classifier according to user profile features to evaluate user suspicious probability in the database. Secondly, we construct singular value decomposition algorithm based on Hebbian learning and matrix factorization algorithm by integrating user suspicion information. Finally, a dynamic weighting scheme is used in combination with the above algorithm and the collaborative filtering algorithm based on item weight, and the above algorithms are mixed according to a certain weight to obtain a robust collaborative filtering algorithm SRICF. By adjusting the weight ratio, advantages of each algorithm are brought into play, thereby improving recommendation accuracy and robustness of the algorithm. Experimental results show that our algorithm has good prediction accuracy and robustness compared with other single algorithms.

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“…Later, they apply K-means clustering to develop product recommendation systems for e-commerce business applications. Similarly, kernel principal component analysis is utilized in [17] to diminish the dimension of rating matrix and then fuzzy c-means clustering is applied to cluster user profiles for improving the recommendation accuracy. Also, in [18], Kmeans and SVD algorithms are applied to reduce dimensionality and cluster similar users to generate more accurate recommendations.…”

Section: Introductionmentioning

confidence: 99%

Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

Binbusayyis¹

2022

Intelligent Automation &Amp; Soft Computing

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The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating sparsity and cold-start. Inspired by the breakthrough results and great strides of deep learning in a wide variety of applications, this work for the first time proposes a deep clustering model leveraging the strength of deep learning and fuzzy clustering for user-based collaborative filtering (UCF). At first, the proposed model applies deep autoencoder (AE) to learn users' latent feature representation from the original rating matrix and then conducts fuzzy clustering assignments on learnt users' latent representation to form user clusters. More importantly, the proposed model defines a unified objective function combining the reconstruction loss and clustering loss to yield end-toend UCF process that can jointly optimize the process of data representation learning and clustering assignment. The effectiveness of the proposed model is evaluated with two real-world datasets, MoiveLens100K and MovieLens1M using two standard performance metrics. Experimental results of the proposed model demonstrated the prospects for gain in top-K recommendation accuracy against recent related works with resistant to rating sparsity and cold start problems.

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Robust Recommendation Algorithm Based on Kernel Principal Component Analysis and Fuzzy C-means Clustering

Cited by 16 publications

References 15 publications

The Application of Personalized Recommendation System in the Cross-Regional Promotion of Provincial Intangible Cultural Heritage

The Application of Personalized Recommendation System in the Cross-Regional Promotion of Provincial Intangible Cultural Heritage

Robust Recommendation Algorithm Based on User Suspicious Probability and Item Weight

Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

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